Consistent negative temperature sensitivity and positive influence of precipitation on growth of floodplain <i>Picea glauca</i> in Interior Alaska
Bibliographic record
Abstract
This paper calibrates climate controls over radial growth of floodplain white spruce ( Picea glauca (Moench) Voss) and examines whether growth in these populations responds similarly to climate as upland trees in Interior Alaska. Floodplain white spruce trees hold previously unrecognized potential for long-term climate reconstruction because they are the source of driftwood that becomes frozen in coastal deposits, where archeological timbers and beach logs represent well-preserved datable material. We compared ring width chronologies for 135 trees in six stands on the Yukon Flats and Tanana River with temperature and precipitation at Fairbanks from 1912–2001. Our sample contains a stable common signal representing a strong negative relationship between summer temperature and tree growth. We developed a floodplain temperature index (FPTI), which explains half of the variability of the composite chronology, and a supplemental precipitation index (SPI) based on correlation of monthly precipitation with the residual of the temperature-based prediction of growth. We then combined FPTI and SPI into a climate favorability index (CFI) in which above-normal precipitation partially compensates for temperature-induced drought reduction of growth and vice versa. CFI and growth have been particularly low since 1969. Our results provide a basis for building longer chronologies based on archeological wood and for projecting future growth.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".